He Chengwen, Yuan Yunbin, Tan Bingfeng
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Feb 13;21(4):1321. doi: 10.3390/s21041321.
Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the effects of NLOS bias for improve positioning accuracy and speed up calculation via an iterative method. We can transform the TOA-based positioning problem into a sparse optimization one under mixed sparse LOS/NLOS conditions if we consider NLOS bias as outliers. Thus, a relatively good method to deal with sparse localization problem is L1 norm. Compared with some existing methods, the proposed method not only has the advantages of simple and intuitive principle, but also can neglect NLOS status and corresponding NLOS errors. Experimental results show that our algorithm performs well in terms of computational time and positioning accuracy.
在混合稀疏视距/非视距（LOS/NLOS）条件下，如何快速实现高精度定位仍是一项具有挑战性的任务，也是过去十几年中的一个关键问题。为了解决这个问题，我们提出了一种约束L1范数最小化方法，该方法可以通过迭代方法减少非视距偏差的影响，以提高定位精度并加快计算速度。如果将非视距偏差视为异常值，我们可以在混合稀疏视距/非视距条件下将基于到达时间（TOA）的定位问题转化为一个稀疏优化问题。因此，处理稀疏定位问题的一种相对较好的方法是L1范数。与一些现有方法相比，所提方法不仅具有原理简单直观的优点，而且可以忽略非视距状态及相应的非视距误差。实验结果表明，我们的算法在计算时间和定位精度方面表现良好。
